{
 "metadata": {
  "name": "",
  "signature": "sha256:5aebaa8fb449a419e62c4c373864fcd3d1a4786df1920c409888606d59f51194"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Creating the dataset in nilmtk format"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from nilmtk.dataset_converters.iawe.convert_iawe import  convert_iawe \n",
      "convert_iawe(\"/home/nipun/Copy/Dataset/\", \"/home/nipun/Desktop/iawe.h5\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Loading  1\n",
        "Loading "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 2\n",
        "Loading "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 3\n",
        "Loading "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 4\n",
        "Loading "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 5\n",
        "Loading "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 6\n",
        "Loading "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 7\n",
        "Loading "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 8\n",
        "Loading "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 9\n",
        "Loading "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 10\n",
        "Loading "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 11\n",
        "Loading "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 12\n",
        "Done converting YAML metadata to HDF5!"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done converting iAWE to HDF5!\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/home/nipun/anaconda/lib/python2.7/site-packages/pandas/io/parsers.py:1139: DtypeWarning: Columns (1,2,3,4,5,6,7) have mixed types. Specify dtype option on import or set low_memory=False.\n",
        "  data = self._reader.read(nrows)\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from nilmtk import DataSet"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "iawe = DataSet(\"/home/nipun/Copy/Dataset/iawe.h5\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "iawe.buildings[1].elec"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "MeterGroup(meters=\n",
        "  ElecMeter(instance=1, building=1, dataset='iAWE', site_meter, appliances=[])\n",
        "  ElecMeter(instance=2, building=1, dataset='iAWE', site_meter, appliances=[])\n",
        "  ElecMeter(instance=3, building=1, dataset='iAWE', appliances=[Appliance(type='fridge', instance=1)])\n",
        "  ElecMeter(instance=4, building=1, dataset='iAWE', appliances=[Appliance(type='air conditioner', instance=1)])\n",
        "  ElecMeter(instance=5, building=1, dataset='iAWE', appliances=[Appliance(type='air conditioner', instance=2)])\n",
        "  ElecMeter(instance=6, building=1, dataset='iAWE', appliances=[Appliance(type='washing machine', instance=1)])\n",
        "  ElecMeter(instance=7, building=1, dataset='iAWE', appliances=[Appliance(type='computer', instance=1)])\n",
        "  ElecMeter(instance=8, building=1, dataset='iAWE', appliances=[Appliance(type='clothes iron', instance=1)])\n",
        "  ElecMeter(instance=9, building=1, dataset='iAWE', appliances=[Appliance(type='unknown', instance=1)])\n",
        "  ElecMeter(instance=10, building=1, dataset='iAWE', appliances=[Appliance(type='television', instance=1)])\n",
        "  ElecMeter(instance=11, building=1, dataset='iAWE', appliances=[Appliance(type='wet appliance', instance=1)])\n",
        "  ElecMeter(instance=12, building=1, dataset='iAWE', appliances=[Appliance(type='motor', instance=1)])\n",
        ")"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "iawe.buildings[1].elec.draw_wiring_graph()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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RSL2EBHID0cCBXLmYZ2ND1apV+eK//6VFixZax80WpHyFeA69Xk9oaCjBwcGsX7+eihUr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\nKF2kCCfj4ij8Cs9vkjs3H//+O507d87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       "text": [
        "<matplotlib.figure.Figure at 0x7f3ef1687450>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "elec = iawe.buildings[1].elec"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "elec.mains().good_sections()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/home/nipun/git/nilmtk/nilmtk/metergroup.py:730: UserWarning: As a quick implementation we only get Good Sections from the first meter in the meter group.  We should really return the intersection of the good sections for all meters.  This will be fixed...\n",
        "  warn(\"As a quick implementation we only get Good Sections from\"\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "[TimeFrame(start='2013-05-24 05:30:00+05:30', end='2013-05-24 07:13:35+05:30', empty=False),\n",
        " TimeFrame(start='2013-05-24 09:07:19+05:30', end='2013-05-25 12:07:19+05:30', empty=False),\n",
        " TimeFrame(start='2013-05-25 13:38:35+05:30', end='2013-05-25 14:08:06+05:30', empty=False),\n",
        " TimeFrame(start='2013-05-25 14:36:51+05:30', end='2013-05-25 18:06:43+05:30', empty=False),\n",
        " TimeFrame(start='2013-05-25 19:07:08+05:30', end='2013-05-26 00:56:38+05:30', empty=False),\n",
        " TimeFrame(start='2013-05-26 01:50:55+05:30', end='2013-05-26 15:55:34+05:30', empty=False),\n",
        " TimeFrame(start='2013-05-26 16:18:08+05:30', end='2013-05-26 23:37:08+05:30', empty=False),\n",
        " TimeFrame(start='2013-05-27 00:12:16+05:30', end='2013-05-27 11:02:20+05:30', empty=False),\n",
        " TimeFrame(start='2013-05-27 11:25:28+05:30', end='2013-05-27 18:13:53+05:30', empty=False),\n",
        " TimeFrame(start='2013-05-27 18:26:24+05:30', end='2013-05-27 18:45:21+05:30', empty=False),\n",
        " TimeFrame(start='2013-05-27 19:25:30+05:30', end='2013-05-28 00:21:33+05:30', empty=False),\n",
        " TimeFrame(start='2013-05-28 00:39:35+05:30', end='2013-05-28 07:58:23+05:30', empty=False),\n",
        " TimeFrame(start='2013-05-28 08:47:21+05:30', end='2013-05-28 08:47:24+05:30', empty=False),\n",
        " TimeFrame(start='2013-05-28 08:52:49+05:30', end='2013-05-28 16:11:30+05:30', empty=False),\n",
        " TimeFrame(start='2013-05-28 16:25:35+05:30', end='2013-05-28 16:55:35+05:30', empty=False),\n",
        " TimeFrame(start='2013-05-28 17:10:37+05:30', end='2013-05-28 17:28:32+05:30', empty=False),\n",
        " TimeFrame(start='2013-05-28 18:21:51+05:30', end='2013-05-29 06:08:37+05:30', empty=False),\n",
        " TimeFrame(start='2013-05-29 06:32:54+05:30', end='2013-05-30 01:08:55+05:30', empty=False),\n",
        " TimeFrame(start='2013-05-30 02:38:09+05:30', end='2013-05-30 22:36:55+05:30', empty=False),\n",
        " TimeFrame(start='2013-05-30 23:22:40+05:30', end='2013-05-31 09:55:55+05:30', empty=False),\n",
        " TimeFrame(start='2013-05-31 11:53:57+05:30', end='2013-05-31 17:49:39+05:30', empty=False),\n",
        " TimeFrame(start='2013-05-31 18:11:54+05:30', end='2013-06-01 07:55:15+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-01 08:11:46+05:30', end='2013-06-01 08:21:47+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-01 10:25:35+05:30', end='2013-06-01 11:41:46+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-01 11:52:40+05:30', end='2013-06-01 12:05:10+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-01 12:16:04+05:30', end='2013-06-01 22:09:20+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-02 06:49:00+05:30', end='2013-06-03 09:04:50+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-03 09:47:22+05:30', end='2013-06-03 09:47:45+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-03 10:10:47+05:30', end='2013-06-04 10:02:26+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-04 12:03:12+05:30', end='2013-06-04 13:03:15+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-04 13:18:17+05:30', end='2013-06-04 13:46:25+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-04 14:43:57+05:30', end='2013-06-04 17:31:41+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-04 18:16:09+05:30', end='2013-06-05 01:23:05+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-05 02:46:30+05:30', end='2013-06-05 23:23:05+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-06 04:43:05+05:30', end='2013-06-06 06:41:39+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-06 08:13:12+05:30', end='2013-06-06 13:31:45+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-06 15:12:30+05:30', end='2013-06-06 19:24:42+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-06 23:23:51+05:30', end='2013-06-07 06:02:33+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-07 07:00:03+05:30', end='2013-06-07 08:32:44+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-07 08:41:28+05:30', end='2013-06-08 12:35:47+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-08 13:19:04+05:30', end='2013-06-08 13:41:49+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-08 15:02:19+05:30', end='2013-06-08 16:26:21+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-08 16:46:14+05:30', end='2013-06-08 20:37:18+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-08 22:27:43+05:30', end='2013-06-08 23:39:15+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-09 00:16:27+05:30', end='2013-06-09 08:07:47+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-09 08:46:02+05:30', end='2013-06-09 10:13:52+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-09 11:10:56+05:30', end='2013-06-09 17:05:41+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-09 17:27:56+05:30', end='2013-06-09 18:17:50+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-09 18:37:24+05:30', end='2013-06-09 22:28:20+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-10 00:22:41+05:30', end='2013-06-10 17:32:23+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-10 18:03:17+05:30', end='2013-06-10 18:21:17+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-10 18:57:56+05:30', end='2013-06-11 00:15:39+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-11 02:42:00+05:30', end='2013-06-11 07:04:25+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-11 07:34:03+05:30', end='2013-06-11 18:13:21+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-11 18:48:46+05:30', end='2013-06-12 13:31:31+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-12 13:43:09+05:30', end='2013-06-12 14:18:30+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-12 16:13:30+05:30', end='2013-06-12 20:07:40+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-12 20:39:26+05:30', end='2013-06-14 14:43:50+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-14 15:04:10+05:30', end='2013-06-16 13:38:05+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-16 16:28:22+05:30', end='2013-06-16 17:35:15+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-16 20:52:08+05:30', end='2013-06-16 22:19:17+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-16 22:41:03+05:30', end='2013-06-19 10:57:51+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-19 14:29:42+05:30', end='2013-06-19 20:28:10+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-19 20:59:48+05:30', end='2013-06-20 14:18:00+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-20 14:37:49+05:30', end='2013-06-22 00:22:18+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-22 01:25:27+05:30', end='2013-06-22 22:39:14+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-23 00:09:59+05:30', end='2013-06-23 00:14:19+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-23 00:36:38+05:30', end='2013-06-25 10:33:56+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-25 11:36:00+05:30', end='2013-06-26 10:45:34+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-26 13:20:26+05:30', end='2013-06-26 17:23:01+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-26 17:36:39+05:30', end='2013-06-26 19:29:57+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-26 19:36:37+05:30', end='2013-06-26 19:55:02+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-26 20:03:42+05:30', end='2013-06-28 09:40:24+05:30', empty=False),\n",
        " TimeFrame(start='2013-06-28 10:14:31+05:30', end='2013-07-01 14:28:57+05:30', empty=False),\n",
        " TimeFrame(start='2013-07-01 15:22:33+05:30', end='2013-07-02 04:31:28+05:30', empty=False),\n",
        " TimeFrame(start='2013-07-02 05:29:38+05:30', end='2013-07-03 08:42:38+05:30', empty=False),\n",
        " TimeFrame(start='2013-07-03 08:55:51+05:30', end='2013-07-04 10:12:17+05:30', empty=False),\n",
        " TimeFrame(start='2013-07-04 10:20:22+05:30', end='2013-07-05 14:28:12+05:30', empty=False),\n",
        " TimeFrame(start='2013-07-05 14:38:59+05:30', end='2013-07-05 18:40:26+05:30', empty=False),\n",
        " TimeFrame(start='2013-07-05 19:31:50+05:30', end='2013-07-06 06:23:01+05:30', empty=False),\n",
        " TimeFrame(start='2013-07-06 07:15:25+05:30', end='2013-07-06 10:59:03+05:30', empty=False),\n",
        " TimeFrame(start='2013-07-06 11:05:37+05:30', end='2013-07-06 12:05:39+05:30', empty=False),\n",
        " TimeFrame(start='2013-07-06 12:20:56+05:30', end='2013-07-06 12:35:56+05:30', empty=False),\n",
        " TimeFrame(start='2013-07-06 12:47:21+05:30', end='2013-07-06 13:17:57+05:30', empty=False),\n",
        " TimeFrame(start='2013-07-06 13:48:48+05:30', end='2013-07-06 15:16:06+05:30', empty=False),\n",
        " TimeFrame(start='2013-07-06 15:37:15+05:30', end='2013-07-06 17:56:26+05:30', empty=False),\n",
        " TimeFrame(start='2013-07-06 18:15:37+05:30', end='2013-07-09 11:45:03+05:30', empty=False),\n",
        " TimeFrame(start='2013-07-09 13:22:46+05:30', end='2013-07-11 23:00:06+05:30', empty=False),\n",
        " TimeFrame(start='2013-07-12 01:01:55+05:30', end='2013-07-12 10:41:01+05:30', empty=False),\n",
        " TimeFrame(start='2013-07-12 17:40:31+05:30', end='2013-07-13 05:00:34+05:30', empty=False),\n",
        " TimeFrame(start='2013-07-13 06:31:29+05:30', end='2013-07-14 11:56:17+05:30', empty=False),\n",
        " TimeFrame(start='2013-07-14 12:48:08+05:30', end='2013-07-17 00:36:02+05:30', empty=False),\n",
        " TimeFrame(start='2013-07-17 00:46:40+05:30', end='2013-07-18 10:20:31+05:30', empty=False),\n",
        " TimeFrame(start='2013-07-18 10:31:10+05:30', end='2013-07-20 09:50:07+05:30', empty=False),\n",
        " TimeFrame(start='2013-07-20 09:56:46+05:30', end='2013-07-20 11:12:13+05:30', empty=False),\n",
        " TimeFrame(start='2013-07-20 11:25:10+05:30', end='2013-07-22 01:15:18+05:30', empty=False),\n",
        " TimeFrame(start='2013-07-22 02:01:45+05:30', end='2013-07-24 23:22:40+05:30', empty=False),\n",
        " TimeFrame(start='2013-07-24 23:39:26+05:30', end='2013-07-25 10:28:30+05:30', empty=False),\n",
        " TimeFrame(start='2013-07-25 11:08:23+05:30', end='2013-07-27 10:35:55+05:30', empty=False),\n",
        " TimeFrame(start='2013-07-27 11:14:34+05:30', end='2013-07-29 01:37:31+05:30', empty=False),\n",
        " TimeFrame(start='2013-07-29 03:24:09+05:30', end='2013-07-30 14:27:06+05:30', empty=False),\n",
        " TimeFrame(start='2013-07-30 14:46:54+05:30', end='2013-08-03 03:49:56+05:30', empty=False),\n",
        " TimeFrame(start='2013-08-03 06:08:35+05:30', end='2013-08-04 00:29:13+05:30', empty=False),\n",
        " TimeFrame(start='2013-08-04 01:47:45+05:30', end='2013-08-04 11:33:47+05:30', empty=False),\n",
        " TimeFrame(start='2013-08-04 12:20:03+05:30', end='2013-08-05 21:03:16+05:30', empty=False)]"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import time"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pycallgraph import PyCallGraph\n",
      "from pycallgraph.output import GraphvizOutput\n",
      "\n",
      "graphviz = GraphvizOutput(output_file='filter_none.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "with PyCallGraph(output=graphviz):\n",
      "    elec.mains().power_series_all_data().head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import Image\n",
      "Image('filter_none.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
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UXS4XOw6Rwfh37QKpVAqNINT/ERp8rbkxtc+3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jDz/EoYMHcejTT1E24Rm57MoV/Nfbb6O4rAwvvvQSfkflal5Qws2EjN6EvT09x/2s+fljW7ci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ejs5ELr83Vxgu3113RTXGk/f/rgp0hL34pn955CSko+Pvjgp3O+bqY2Ht69FgMD3ZO2+/pr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\nHZBKJQhVq7EuJxuRYRrI5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       "prompt_number": 16,
       "text": [
        "<IPython.core.display.Image at 0x7ff2307ade50>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      " mains = elec.mains()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mains_good_sections = mains.good_sections()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mains_energy = mains.total_energy(sections=mains_good_sections)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/home/nipun/git/nilmtk/nilmtk/preprocessing/clip.py:50: RuntimeWarning: No measurement limits for ('frequency', '').\n",
        "  warn('No measurement limits for {}.'.format(measurement), RuntimeWarning)\n",
        "/home/nipun/git/nilmtk/nilmtk/preprocessing/clip.py:50: RuntimeWarning: No measurement limits for ('voltage', '').\n",
        "  warn('No measurement limits for {}.'.format(measurement), RuntimeWarning)\n",
        "/home/nipun/git/nilmtk/nilmtk/preprocessing/clip.py:50: RuntimeWarning: No measurement limits for ('pf', '').\n",
        "  warn('No measurement limits for {}.'.format(measurement), RuntimeWarning)\n",
        "/home/nipun/git/nilmtk/nilmtk/preprocessing/clip.py:50: RuntimeWarning: No measurement limits for ('current', '').\n",
        "  warn('No measurement limits for {}.'.format(measurement), RuntimeWarning)\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mains.dataframe_of_meters()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "ValueError",
       "evalue": "Periods overlap: TimeFrame(start='2013-06-25 15:54:30+05:30', end='2013-06-26 10:45:34+05:30', empty=False) TimeFrame(start='2013-06-25 15:54:42+05:30', end='2013-06-26 10:45:34+05:30', empty=False)",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-11-737041b3fe7e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmains\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdataframe_of_meters\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[1;32m/home/nipun/git/nilmtk/nilmtk/metergroup.pyc\u001b[0m in \u001b[0;36mdataframe_of_meters\u001b[1;34m(self, rule)\u001b[0m\n\u001b[0;32m    648\u001b[0m         \u001b[0mmains\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmains\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    649\u001b[0m         \u001b[0mmains_good_sections\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmains\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgood_sections\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 650\u001b[1;33m         \u001b[0mmains_energy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmains\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtotal_energy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msections\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmains_good_sections\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    651\u001b[0m         \u001b[0menergy_ac_type\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mselect_best_ac_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmains_energy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    652\u001b[0m         \u001b[0menergy_threshold\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmains_energy\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0menergy_ac_type\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;36m0.05\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/nipun/git/nilmtk/nilmtk/metergroup.pyc\u001b[0m in \u001b[0;36mtotal_energy\u001b[1;34m(self, **load_kwargs)\u001b[0m\n\u001b[0;32m    547\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    548\u001b[0m         meter_energies = self._collect_stats_on_all_meters(\n\u001b[1;32m--> 549\u001b[1;33m             load_kwargs, 'total_energy', full_results)\n\u001b[0m\u001b[0;32m    550\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    551\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mmeter_energies\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/nipun/git/nilmtk/nilmtk/metergroup.pyc\u001b[0m in \u001b[0;36m_collect_stats_on_all_meters\u001b[1;34m(self, load_kwargs, func, full_results)\u001b[0m\n\u001b[0;32m    562\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mmeter\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmeters\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    563\u001b[0m             single_stat = getattr(meter, func)(full_results=full_results,\n\u001b[1;32m--> 564\u001b[1;33m                                                **load_kwargs)\n\u001b[0m\u001b[0;32m    565\u001b[0m             \u001b[0mcollected_stats\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msingle_stat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    566\u001b[0m             if (full_results and len(self.meters) > 1 and\n",
        "\u001b[1;32m/home/nipun/git/nilmtk/nilmtk/elecmeter.pyc\u001b[0m in \u001b[0;36mtotal_energy\u001b[1;34m(self, **loader_kwargs)\u001b[0m\n\u001b[0;32m    345\u001b[0m         \u001b[0mclipped\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mClip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msource_node\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    346\u001b[0m         \u001b[0mtotal_energy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTotalEnergy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mclipped\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 347\u001b[1;33m         \u001b[0mtotal_energy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    348\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mfull_results\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    349\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mtotal_energy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/nipun/git/nilmtk/nilmtk/node.pyc\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     38\u001b[0m         \"\"\"Pulls data through the pipeline.  Useful if we just want to calculate \n\u001b[0;32m     39\u001b[0m         some stats.\"\"\"\n\u001b[1;32m---> 40\u001b[1;33m         \u001b[1;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprocess\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     41\u001b[0m             \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     42\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/nipun/git/nilmtk/nilmtk/stats/totalenergy.pyc\u001b[0m in \u001b[0;36mprocess\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     27\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mchunk\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupstream\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprocess\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     28\u001b[0m             \u001b[0menergy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_total_energy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchunk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax_sample_period\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 29\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchunk\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtimeframe\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0menergy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     30\u001b[0m             \u001b[1;32myield\u001b[0m \u001b[0mchunk\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     31\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/nipun/git/nilmtk/nilmtk/stats/totalenergyresults.pyc\u001b[0m in \u001b[0;36mappend\u001b[1;34m(self, timeframe, new_results)\u001b[0m\n\u001b[0;32m     21\u001b[0m             raise KeyError('new_results must be a combination of ' +\n\u001b[0;32m     22\u001b[0m                            str(AC_TYPES))\n\u001b[1;32m---> 23\u001b[1;33m         \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mTotalEnergyResults\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeframe\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnew_results\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     24\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     25\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0munify\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mother\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/nipun/git/nilmtk/nilmtk/results.pyc\u001b[0m in \u001b[0;36mappend\u001b[1;34m(self, timeframe, new_results)\u001b[0m\n\u001b[0;32m     61\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mintersect\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m                 raise ValueError(\"Periods overlap: \" + str(tf) + \n\u001b[1;32m---> 63\u001b[1;33m                                  \" \" + str(timeframe))\n\u001b[0m\u001b[0;32m     64\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m         row = pd.DataFrame(index=[timeframe.start],\n",
        "\u001b[1;31mValueError\u001b[0m: Periods overlap: TimeFrame(start='2013-06-25 15:54:30+05:30', end='2013-06-26 10:45:34+05:30', empty=False) TimeFrame(start='2013-06-25 15:54:42+05:30', end='2013-06-26 10:45:34+05:30', empty=False)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "> \u001b[1;32m/home/nipun/git/nilmtk/nilmtk/results.py\u001b[0m(63)\u001b[0;36mappend\u001b[1;34m()\u001b[0m\n",
        "\u001b[1;32m     62 \u001b[1;33m                raise ValueError(\"Periods overlap: \" + str(tf) + \n",
        "\u001b[0m\u001b[1;32m---> 63 \u001b[1;33m                                 \" \" + str(timeframe))\n",
        "\u001b[0m\u001b[1;32m     64 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[0m\n"
       ]
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "ipdb> ?\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Documented commands (type help <topic>):\n",
        "========================================\n",
        "EOF    bt         cont      enable  jump  pdef    psource  run      unt   \n",
        "a      c          continue  exit    l     pdoc    q        s        until \n",
        "alias  cl         d         h       list  pfile   quit     step     up    \n",
        "args   clear      debug     help    n     pinfo   r        tbreak   w     \n",
        "b      commands   disable   ignore  next  pinfo2  restart  u        whatis\n",
        "break  condition  down      j       p     pp      return   unalias  where \n",
        "\n",
        "Miscellaneous help topics:\n",
        "==========================\n",
        "exec  pdb\n",
        "\n",
        "Undocumented commands:\n",
        "======================\n",
        "retval  rv\n",
        "\n"
       ]
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "ipdb> p mains_good_sections\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "*** NameError: NameError(\"name 'mains_good_sections' is not defined\",)\n"
       ]
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "ipdb> q\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import time\n",
      "start = time.time()\n",
      "print elec.proportion_of_energy_submetered()\n",
      "end = time.time()\n",
      "print end - start, \"s\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Getting total energy for ('fridge', 1)\n",
        "  total energy ="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " active      80.463459\n",
        "reactive    52.435464\n",
        "apparent    97.008218\n",
        "dtype: float64\n",
        "Getting total energy for ('television', 1)\n",
        "  total energy ="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " active      10.926226\n",
        "reactive     0.000116\n",
        "apparent    11.651691\n",
        "dtype: float64\n",
        "Getting total energy for ('wet appliance', 1)\n",
        "  total energy ="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " active      0.543758\n",
        "reactive    2.040104\n",
        "apparent    2.249115\n",
        "dtype: float64\n",
        "Getting total energy for ('washing machine', 1)\n",
        "  total energy ="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " active      1.842086\n",
        "reactive    0.008720\n",
        "apparent    1.860645\n",
        "dtype: float64\n",
        "Getting total energy for ('air conditioner', 1)\n",
        "  total energy ="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " active      191.952256\n",
        "reactive     39.911030\n",
        "apparent    196.658907\n",
        "dtype: float64\n",
        "Getting total energy for ('clothes iron', 1)\n",
        "  total energy ="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " active      0.786596\n",
        "reactive    0.000328\n",
        "apparent    0.806191\n",
        "dtype: float64\n",
        "Getting total energy for ('computer', 1)\n",
        "  total energy ="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " active      23.358390\n",
        "reactive     0.001638\n",
        "apparent    41.373663\n",
        "dtype: float64\n",
        "Getting total energy for ('air conditioner', 2)\n",
        "  total energy ="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " active      194.012510\n",
        "reactive     33.438748\n",
        "apparent    197.279263\n",
        "dtype: float64\n",
        "Getting total energy for ('motor', 1)\n",
        "  total energy ="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " active    1.047471\n",
        "dtype: float64\n",
        "Getting total energy for ('unknown', 1)\n",
        "  total energy ="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " active      0.556922\n",
        "reactive    0.208699\n",
        "apparent    0.635201\n",
        "dtype: float64\n",
        "Using AC type 'active' from mains."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "0.370032075004\n",
        "163.649049997 s\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "start = time.time()\n",
      "elec.total_energy()\n",
      "end  = time.time()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print elec.total_energy()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "active      1897.730371\n",
        "reactive     262.570156\n",
        "apparent    2009.394436\n",
        "dtype: float64\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print end - start"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "7.19227719307\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mains = elec.mains()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m = mains.meters[1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m.power_series_all_data()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "2013-05-24 05:30:00+05:30    282.744995\n",
        "2013-05-24 05:30:01+05:30    288.757996\n",
        "2013-05-24 05:30:02+05:30    288.757996\n",
        "2013-05-24 05:30:03+05:30    297.394012\n",
        "2013-05-24 05:30:04+05:30    303.722992\n",
        "2013-05-24 05:30:05+05:30    297.273987\n",
        "2013-05-24 05:30:06+05:30    297.330994\n",
        "2013-05-24 05:30:07+05:30    297.330994\n",
        "2013-05-24 05:30:08+05:30    282.475006\n",
        "2013-05-24 05:30:09+05:30    282.580994\n",
        "2013-05-24 05:30:10+05:30    282.899994\n",
        "2013-05-24 05:30:11+05:30    282.915985\n",
        "2013-05-24 05:30:12+05:30    283.238007\n",
        "2013-05-24 05:30:13+05:30    283.238007\n",
        "2013-05-24 05:30:14+05:30    283.174011\n",
        "...\n",
        "2013-08-05 21:02:46+05:30    696.109985\n",
        "2013-08-05 21:02:49+05:30    696.047974\n",
        "2013-08-05 21:02:50+05:30    695.791992\n",
        "2013-08-05 21:02:53+05:30    695.333008\n",
        "2013-08-05 21:02:54+05:30    695.291992\n",
        "2013-08-05 21:02:57+05:30    695.656982\n",
        "2013-08-05 21:02:58+05:30    695.742004\n",
        "2013-08-05 21:03:01+05:30    696.257019\n",
        "2013-08-05 21:03:04+05:30    696.877991\n",
        "2013-08-05 21:03:07+05:30    696.685974\n",
        "2013-08-05 21:03:09+05:30    696.762024\n",
        "2013-08-05 21:03:10+05:30    696.864990\n",
        "2013-08-05 21:03:11+05:30    696.864990\n",
        "2013-08-05 21:03:12+05:30    697.007996\n",
        "2013-08-05 21:03:15+05:30    697.237976\n",
        "Name: (power, active), Length: 4391004"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m.total_energy()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "active      1005.572193\n",
        "reactive      96.815441\n",
        "apparent    1043.941205\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import time\n",
      "start  =  time.time()\n",
      "elec.fraction_per_meter()\n",
      "end  =  time.time()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/home/nipun/git/nilmtk/nilmtk/preprocessing/clip.py:50: RuntimeWarning: No measurement limits for ('frequency', '').\n",
        "  warn('No measurement limits for {}.'.format(measurement), RuntimeWarning)\n",
        "/home/nipun/git/nilmtk/nilmtk/preprocessing/clip.py:50: RuntimeWarning: No measurement limits for ('voltage', '').\n",
        "  warn('No measurement limits for {}.'.format(measurement), RuntimeWarning)\n",
        "/home/nipun/git/nilmtk/nilmtk/preprocessing/clip.py:50: RuntimeWarning: No measurement limits for ('pf', '').\n",
        "  warn('No measurement limits for {}.'.format(measurement), RuntimeWarning)\n",
        "/home/nipun/git/nilmtk/nilmtk/preprocessing/clip.py:50: RuntimeWarning: No measurement limits for ('current', '').\n",
        "  warn('No measurement limits for {}.'.format(measurement), RuntimeWarning)\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print elec.fraction_per_meter()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1/12 ElecMeter(instance=1, building=1, dataset='iAWE', site_meter, appliances=[])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "2/12 ElecMeter(instance=2, building=1, dataset='iAWE', site_meter, appliances=[])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "3/12 ElecMeter(instance=3, building=1, dataset='iAWE', appliances=[Appliance(type='fridge', instance=1)])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "4/12 ElecMeter(instance=4, building=1, dataset='iAWE', appliances=[Appliance(type='air conditioner', instance=1)])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "5/12 ElecMeter(instance=5, building=1, dataset='iAWE', appliances=[Appliance(type='air conditioner', instance=2)])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "6/12 ElecMeter(instance=6, building=1, dataset='iAWE', appliances=[Appliance(type='washing machine', instance=1)])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "7/12 ElecMeter(instance=7, building=1, dataset='iAWE', appliances=[Appliance(type='computer', instance=1)])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "8/12 ElecMeter(instance=8, building=1, dataset='iAWE', appliances=[Appliance(type='clothes iron', instance=1)])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "9/12 ElecMeter(instance=9, building=1, dataset='iAWE', appliances=[Appliance(type='unknown', instance=1)])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "10/12 ElecMeter(instance=10, building=1, dataset='iAWE', appliances=[Appliance(type='television', instance=1)])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "11/12 ElecMeter(instance=11, building=1, dataset='iAWE', appliances=[Appliance(type='wet appliance', instance=1)])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "12/12 ElecMeter(instance=12, building=1, dataset='iAWE', appliances=[Appliance(type='motor', instance=1)])"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1                     NaN\n",
        "2                     NaN\n",
        "3                     NaN\n",
        "4                     NaN\n",
        "5                     NaN\n",
        "6                     NaN\n",
        "7                     NaN\n",
        "8                     NaN\n",
        "9                     NaN\n",
        "10                    NaN\n",
        "11                    NaN\n",
        "12                    NaN\n",
        "(1, 1, iAWE)     0.192552\n",
        "(2, 1, iAWE)     0.519530\n",
        "(3, 1, iAWE)     0.049750\n",
        "(4, 1, iAWE)     0.103877\n",
        "(5, 1, iAWE)     0.103186\n",
        "(6, 1, iAWE)     0.001082\n",
        "(7, 1, iAWE)     0.021384\n",
        "(8, 1, iAWE)     0.000453\n",
        "(9, 1, iAWE)     0.000403\n",
        "(10, 1, iAWE)    0.006101\n",
        "(11, 1, iAWE)    0.001146\n",
        "(12, 1, iAWE)    0.000536\n",
        "dtype: float64\n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print end - start"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "7.8085038662\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "elec.select_top_k(k=4)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
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        "      <th></th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "      <th>3</th>\n",
        "      <th>4</th>\n",
        "      <th>5</th>\n",
        "      <th>6</th>\n",
        "      <th>7</th>\n",
        "      <th>8</th>\n",
        "      <th>9</th>\n",
        "      <th>10</th>\n",
        "      <th>11</th>\n",
        "      <th>12</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>active</th>\n",
        "      <td> 360.496060</td>\n",
        "      <td> 1005.572193</td>\n",
        "      <td> 82.958314</td>\n",
        "      <td> 203.569523</td>\n",
        "      <td> 203.889213</td>\n",
        "      <td> 2.152638</td>\n",
        "      <td> 24.351495</td>\n",
        "      <td> 0.885766</td>\n",
        "      <td> 0.722616</td>\n",
        "      <td> 11.495919</td>\n",
        "      <td> 0.558716</td>\n",
        "      <td> 1.077918</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>apparent</th>\n",
        "      <td> 386.911386</td>\n",
        "      <td> 1043.941205</td>\n",
        "      <td> 99.966983</td>\n",
        "      <td> 208.730314</td>\n",
        "      <td> 207.341398</td>\n",
        "      <td> 2.173996</td>\n",
        "      <td> 42.969043</td>\n",
        "      <td> 0.911080</td>\n",
        "      <td> 0.810098</td>\n",
        "      <td> 12.258396</td>\n",
        "      <td> 2.302620</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>reactive</th>\n",
        "      <td>  30.660539</td>\n",
        "      <td>   96.815441</td>\n",
        "      <td> 53.965398</td>\n",
        "      <td>  42.482991</td>\n",
        "      <td>  35.209142</td>\n",
        "      <td> 0.018759</td>\n",
        "      <td>  0.002313</td>\n",
        "      <td> 0.000528</td>\n",
        "      <td> 0.249570</td>\n",
        "      <td>  0.000176</td>\n",
        "      <td> 2.087380</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "                  1            2          3           4           5   \\\n",
        "active    360.496060  1005.572193  82.958314  203.569523  203.889213   \n",
        "apparent  386.911386  1043.941205  99.966983  208.730314  207.341398   \n",
        "reactive   30.660539    96.815441  53.965398   42.482991   35.209142   \n",
        "\n",
        "                6          7         8         9          10        11  \\\n",
        "active    2.152638  24.351495  0.885766  0.722616  11.495919  0.558716   \n",
        "apparent  2.173996  42.969043  0.911080  0.810098  12.258396  2.302620   \n",
        "reactive  0.018759   0.002313  0.000528  0.249570   0.000176  2.087380   \n",
        "\n",
        "                12  \n",
        "active    1.077918  \n",
        "apparent       NaN  \n",
        "reactive       NaN  "
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "elec.energy_per_meter()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
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        "      <th></th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "      <th>3</th>\n",
        "      <th>4</th>\n",
        "      <th>5</th>\n",
        "      <th>6</th>\n",
        "      <th>7</th>\n",
        "      <th>8</th>\n",
        "      <th>9</th>\n",
        "      <th>10</th>\n",
        "      <th>11</th>\n",
        "      <th>12</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>active</th>\n",
        "      <td> 360.496060</td>\n",
        "      <td> 1005.572193</td>\n",
        "      <td> 82.958314</td>\n",
        "      <td> 203.569523</td>\n",
        "      <td> 203.889213</td>\n",
        "      <td> 2.152638</td>\n",
        "      <td> 24.351495</td>\n",
        "      <td> 0.885766</td>\n",
        "      <td> 0.722616</td>\n",
        "      <td> 11.495919</td>\n",
        "      <td> 0.558716</td>\n",
        "      <td> 1.077918</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>apparent</th>\n",
        "      <td> 386.911386</td>\n",
        "      <td> 1043.941205</td>\n",
        "      <td> 99.966983</td>\n",
        "      <td> 208.730314</td>\n",
        "      <td> 207.341398</td>\n",
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        "      <td> 42.969043</td>\n",
        "      <td> 0.911080</td>\n",
        "      <td> 0.810098</td>\n",
        "      <td> 12.258396</td>\n",
        "      <td> 2.302620</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>reactive</th>\n",
        "      <td>  30.660539</td>\n",
        "      <td>   96.815441</td>\n",
        "      <td> 53.965398</td>\n",
        "      <td>  42.482991</td>\n",
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        "      <td>  0.002313</td>\n",
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        "      <td> 0.249570</td>\n",
        "      <td>  0.000176</td>\n",
        "      <td> 2.087380</td>\n",
        "      <td>      NaN</td>\n",
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        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "                  1            2          3           4           5   \\\n",
        "active    360.496060  1005.572193  82.958314  203.569523  203.889213   \n",
        "apparent  386.911386  1043.941205  99.966983  208.730314  207.341398   \n",
        "reactive   30.660539    96.815441  53.965398   42.482991   35.209142   \n",
        "\n",
        "                6          7         8         9          10        11  \\\n",
        "active    2.152638  24.351495  0.885766  0.722616  11.495919  0.558716   \n",
        "apparent  2.173996  42.969043  0.911080  0.810098  12.258396  2.302620   \n",
        "reactive  0.018759   0.002313  0.000528  0.249570   0.000176  2.087380   \n",
        "\n",
        "                12  \n",
        "active    1.077918  \n",
        "apparent       NaN  \n",
        "reactive       NaN  "
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "elec.total_energy()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "active      1897.730371\n",
        "reactive     262.570156\n",
        "apparent    2009.394436\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "elec.total_energy?\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "elec.dropout_rate()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "0.61103563473527689"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}